Compressive Sensing for Urban Radar
With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates.
Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text:
- Covers both ground-based and airborne synthetic aperture radar (SAR) and uses different signal waveforms
- Demonstrates successful applications of compressive sensing for target detection and revealing building interiors
- Describes problems facing urban radar and highlights sparse reconstruction techniques applicable to urban environments
- Deals with both stationary and moving indoor targets in the presence of wall clutter and multipath exploitation
- Provides numerous supporting examples using real data and computational electromagnetic modeling
Featuring 13 chapters written by leading researchers and experts, Compressive Sensing for Urban Radar is a useful and authoritative reference for radar engineers and defense contractors, as well as a seminal work for graduate students and academia.
Why Read This Book
You should read this book if you want practical, radar-specific guidance on applying compressive sensing to the unique challenges of urban environments: sparse reconstruction, limited aperture/bandwidth, multipath and clutter. You will learn how to reduce data acquisition requirements while still achieving high-resolution imaging, localization, and tracking using algorithms and system designs tailored to real-world urban radar.
Who Will Benefit
Graduate students, radar engineers, and signal-processing practitioners with some DSP and radar background who need to build or analyze urban sensing systems that use compressed measurements.
Level: Advanced — Prerequisites: Linear algebra, probability and stochastic processes, basic digital signal processing (FFT, filtering), fundamentals of radar (range/Doppler, basic array theory), and introductory convex optimization; familiarity with MATLAB or Python is helpful.
Key Takeaways
- Implement sparse reconstruction methods (L1/convex, greedy, iterative thresholding, Bayesian) for radar imaging and parameter estimation.
- Design and evaluate compressive sampling schemes in time, frequency, and space (random, structured, and reduced aperture) suited to urban constraints.
- Apply compressive sensing to radar modalities such as range-Doppler processing, SAR/ISAR imaging, MIMO/array processing, and through-wall/indoor localization.
- Analyze performance under noise, clutter, and multipath—using error bounds, Cramér–Rao analysis, and empirical metrics to choose sensing and reconstruction trade-offs.
- Exploit structured sparsity and prior information (group sparsity, joint/ multiple measurement vectors, model-based CS) to improve robustness in dense urban scenes.
- Translate algorithms into practicable systems: select solvers, understand computational complexity, and account for hardware and sampling limitations.
Topics Covered
- 1. Introduction: Urban Radar Challenges and Opportunities
- 2. Fundamentals of Compressive Sensing and Sparse Models
- 3. Radar Signal Models and Sparse Representations (range, Doppler, angle)
- 4. Sensing Matrix Design: Time, Frequency, and Spatial Sampling
- 5. Sparse Reconstruction Algorithms: Convex, Greedy, Iterative, Bayesian
- 6. Compressive Range-Doppler Processing and Target Detection
- 7. CS for SAR/ISAR and High-Resolution Urban Imaging
- 8. MIMO and Array Compressive Techniques for DOA and Localization
- 9. Through-Wall and Indoor Localization: Multipath and Clutter Mitigation
- 10. Performance Analysis: Noise, Resolution, and CRB for Sparse Estimators
- 11. Practical Considerations: Hardware, Sampling Architectures, and Solvers
- 12. Case Studies and Simulations in Urban Scenarios
- 13. Emerging Topics: Structured Sparsity, Adaptive and Distributed CS
- 14. Conclusions and Research Directions
Languages, Platforms & Tools
How It Compares
Unlike general compressive sensing texts such as Eldar & Kutyniok's 'Compressed Sensing' (theory-focused) or Richards' 'Fundamentals of Radar Signal Processing' (radar fundamentals), this book uniquely blends CS theory with hands-on urban radar applications and implementation considerations.
